Synchronized path-integration recalibration but distinct landmark-control dynamics in head direction and CA1 place cells
Bibliographic record
Abstract
Accurate spatial navigation relies on path integration, a process of tracking one's location by integrating self-motion cues. Path integration uses a gain factor relating self-motion signals to displacement on the cognitive map. This gain is plastic, recalibrating rapidly to match perceived displacements relative to external cues. To elucidate the mechanism of recalibration, we simultaneously recorded from place cells, which instantiate the cognitive map, and head direction (HD) cells, thought to orient the map. Persistent conflict between self-motion and visual feedback induced functionally identical recalibration of path-integration gain in the two neural populations during forward locomotion; however, during locomotor immobility accompanied by head-scanning, HD cells did not exhibit recalibration. Moreover, the two populations manifested differential field-shifting dynamics relative to landmarks during recalibration. These results uncover a tightly coordinated yet behavior-dependent recalibration process across the navigation circuit that achieves a robust yet flexible coupling of the internal sense of position and direction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".